• DocumentCode
    1159963
  • Title

    On hidden nodes for neural nets

  • Author

    Mirchandani, Gagan ; Cao, Wei

  • Author_Institution
    Dept. of Comput. Sci. & Electr. Eng., Vermont Univ., Burlington, VT, USA
  • Volume
    36
  • Issue
    5
  • fYear
    1989
  • fDate
    5/1/1989 12:00:00 AM
  • Firstpage
    661
  • Lastpage
    664
  • Abstract
    Recent results indicate that the number of hidden nodes (H) in a feedforward neural net depend only on the number of input training patterns (T). There appear to be conjectures that H is on the order of T-1 and of log2 T. A proof is given that the maximum number of separable regions (M) in the input space is a function of both H and input space dimension (d). The authors also show that H =M -1 and H=log2M are special cases of that formulation. M defines a lower bound on T, the number of input patterns that may be used for training. Application to some experiments are investigated
  • Keywords
    computer graphics; computerised picture processing; neural nets; experiments; feedforward neural net; hidden nodes for neural nets; input space dimension; input training patterns; maximum number of separable regions; multilayered networks; Circuits and systems; Computer science; Feedforward neural networks; Multilayer perceptrons; Neural networks; Pattern classification; Random number generation; Shape; Sonar;
  • fLanguage
    English
  • Journal_Title
    Circuits and Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0098-4094
  • Type

    jour

  • DOI
    10.1109/31.31313
  • Filename
    31313
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1159963